Multi-Objective Extremal Optimization in Processor Load Balancing for Distributed Programs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)


The paper presents a multi-objective load balancing algorithm based on Extremal Optimization in execution of distributed programs. The Extremal Optimization aims in defining task migration as a means for improving balance in loading executive processors with program tasks. In the proposed multi-objective approach three objectives relevant in processor load balancing for distributed applications are jointly optimized. These objectives include: balance in computational load of distributed processors, total volume of inter-processor communication between tasks and task migration metrics. In the proposed Extremal Optimization algorithms a special approach called Guided Search is applied in selection of a new partial solution to be improved. It is supported by some knowledge of the problem in terms of computational and communication loads influenced by task migration. The proposed algorithms are assessed by simulation experiments with distributed execution of program macro data flow graphs.


Extremal Optimization Multi-objective optimization Processor load balancing 


  1. 1.
    Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from co-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999). Morgan Kaufmann, San Francisco, pp. 825–832 (1999)Google Scholar
  2. 2.
    Barker, K., Chrisochoides, N.: An evaluation of a framework for the dynamic load balancing of highly adaptive and irregular parallel applications. In: Proceedings of the ACM/IEEE Conference on Supercomputing, Phoenix. ACM Press (2003)Google Scholar
  3. 3.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Load balancing in distributed applications based on extremal optimization. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 52–61. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  4. 4.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Improving extremal optimization in load balancing by local search. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 51–62. Springer, Heidelberg (2014). Google Scholar
  5. 5.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Extremal optimization applied to load balancing in execution of distributed programs. Appl. Soft Comput. 30, 501–513 (2015)CrossRefGoogle Scholar
  6. 6.
    Taxicab geometry. Accessed 20 Nov 2017
  7. 7.
    Xu, C., Lau, Francis C.M.: Load Balancing in Parallel Computers: Theory and Practice. Kluwer Academic Publishers, Dordrecht (1997)zbMATHGoogle Scholar
  8. 8.
    Khan, R.Z., Ali, J.: Classification of task partitioning and load balancing strategies in distributed parallel computing systems. Int. J. Comput. Appl. 60(17), 48–53 (2012)Google Scholar
  9. 9.
    Mishra, M., Agarwal, S., Mishra, P., Singh, S.: Comparative analysis of various evolutionary techniques of load balancing: a review. Int. J. Comput. Appl. 63(15), 8–13 (2013)Google Scholar
  10. 10.
    Sneppen, K., et al.: Evolution as a self-organized critical phenomenon. Proc. Nat. Acad. Sci. 92, 5209–5213 (1995)CrossRefGoogle Scholar
  11. 11.
    Zeigler, B.: Hierarchical, modular discrete-event modelling in an object-oriented environment. Simulation 49(5), 219–230 (1987)CrossRefGoogle Scholar
  12. 12.
    Roig, C., Ripoll, A., Guirado, F.: A new task graph model for mapping message passing applications. IEEE Trans. Parallel Distrib. Syst. 18(12), 1740–1753 (2007)CrossRefGoogle Scholar
  13. 13.
    Collette, Y., Siarry, P.: Multi-objective Optimization: Principles and Case Studies. Springer, Heidelberg (2004). p. 293zbMATHGoogle Scholar
  14. 14.
    Ehrgott, M.: Multi-criteria Optimization. Springer, Heidelberg (2005). p. 324zbMATHGoogle Scholar
  15. 15.
    Coello Coello, C.A.: Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. Mag. 1, 28–36 (2006)CrossRefGoogle Scholar
  16. 16.
    Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Boston (2007). p. 800zbMATHGoogle Scholar
  17. 17.
    Chen, M.-R., Lu, Y.-Z.: A novel elitist multi-objective optimization algorithm: multi-objective extremal optimization. Shanghai Jiao Tong UniversityGoogle Scholar
  18. 18.
    Ahmed, E., Elettreby, M.F.: On multi-objective evolution model. Int. J. Mod. Phys. C 15(9), 1189–1195 (2004)CrossRefGoogle Scholar
  19. 19.
    Gómez-Meneses, P., Randall, M., Lewis, A.: A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. Bond University, Griffith University, Australia (2010)Google Scholar
  20. 20.
    Galski, R.L., de Sousa, F.L., Ramos, F.M., Muraoka, I.: Spacecraft thermal design with the generalized extremal optimization algorithm. In: Proceedings of Inverse Problems, Design and Optimization Symposium, Rio de Janeiro, Brazil, 2004Google Scholar
  21. 21.
    Chen, M., Lu, Y., Yang, G.: Multi-objective extremal optimization with applications to engineering design. J. Zhejiang Univ. Sci. A 8(12), 1905–1911 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.: A multiobjective extremal optimization algorithm for efficient mapping in grids. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds.) Applications of Soft Computing. Advances in Intelligent and Soft Computing. Springer, Heidelberg (2009). Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of High Performance Computing and NetworkingCNRNaplesItaly
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Université Lille — CRISTAL, CNRSLilleFrance
  4. 4.Polish-Japanese Academy of Information TechnologyWarsawPoland

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